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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳宏銘(Homer H. Chen) | |
| dc.contributor.author | Chih-Yuan Chung | en |
| dc.contributor.author | 鐘志遠 | zh_TW |
| dc.date.accessioned | 2021-06-14T16:45:49Z | - |
| dc.date.available | 2009-08-04 | |
| dc.date.copyright | 2008-08-04 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-30 | |
| dc.identifier.citation | [1] Y. Li, J. Sun, C.-K. Tang, and H.-Y. Shum, “Lazy snapping,” ACM Trans. Graph,
vol. 23, no. 3, pp. 303–308, 2004. [2] J.Wang, P. Bhat, R. A. Colburn, M. A., and M. F. Cohen, “Interactive video cutout,” ACM Trans. Graph, vol. 24, no. 3, pp. 585–594, 2005. [3] Y. Li, J. Sun, and H.-Y. Shum, “Video object cut and paste,” ACM Trans. Graph., vol. 24, no. 3, pp. 595–600, 2005. [4] L. Grady, “Random walks for image segmentation,” IEEE Trans. PAMI, vol. 28, no. 11, pp. 1768–1783, Nov. 2006. [5] M. Gleicher, “Image snapping,” in SIGGRAPH, Robert Cook, Ed., Aug. 1995, Annual Conference Series, pp. 183–190. [6] E. N. Mortensen and W. A. Barrett, “Intelligent scissors for image composition,” in SIGGRAPH, 1995, pp. 191–198. [7] Y. Boykov and M.-P. Jolly, “Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images,” in ICCV, 2001, pp. 105–112. [8] A. K. Sinop and L. Grady, “A seeded image segmentation framework unifying graph cuts and random walker which yields A new algorithm,” in ICCV, 2007, pp. 1–8. [9] X. Bai and G. Sapiro, “A geodesic framework for fast interactive image and video segmentation and matting,” in ICCV, 2007, pp. 1–8. [10] M. Piccardi, “Background subtraction techniques: a review,” in SMC, 2004, pp. 3099–3104. [11] J. Sun, W. W. Zhang, X. Tang, and H. Y. Shum, “Background cut,” in ECCV, 2006, pp. II: 628–641. [12] S. Khan and M. Shah, “Object based segmentation of video using color, motion and spatial information,” in CVPR, 2001, pp. 746–751. [13] J. Y. A.Wang and E. H. Adelson, “Representing moving images with layers,” IEEE Trans. Image Processing, vol. 3, no. 5, pp. 625–638, 1994. [14] S. Ayer and H. S. Sawhney, “Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding,” in ICCV, 1995, pp. 777–784. [15] Y. Su, M.-T. Sun, and V. Hsu, “Global motion estimation from coarsely sampled motion vector field and the applications,” IEEE Trans. CSVT, vol. 15, 2005. [16] A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov, “Bilayer segmentation of live video,” in CVPR, 2006, pp. I: 53–60. [17] P. Yin, A. Criminisi, J. Winn, and I. A. Essa, “Tree-based classifiers for bilayer video segmentation,” in CVPR, 2007, pp. 1–8. [18] V. Kolmogorov and Y. Y. Boykov, “An experimental comparison of min-cut/maxflow algorithms for energy minimization in vision,” in EMMCVPR, 2002, p. 359 ff. [19] L. Vincent and P. Soille, “Watersheds in digital spaces: An efficient algorithm based on immersion simulations,” IEEE Trans. PAMI, vol. 13, no. 6, pp. 583–598, 1991. [20] C. Rother, V. Kolmogorov, and A. Blake, “Grabcut: interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph, vol. 23, no. 3, pp. 309–314, 2004. [21] L. Yatziv, A. Bartesaghi, and G. Sapiro, “O(n) implementation of the fast marching algorithm,” Journal of Computational Physics, vol. 212, pp. 393–399, 2006. [22] J. Sun, J. Jia, C.-K. Tang, and H.-Y. Shum, “Poisson matting,” ACM Trans. Graph, vol. 23, no. 3, pp. 315–321, Aug. 2004. [23] Y.-Y. Chuang, B. Curless, D. Salesin, and Richard Szeliski, “A bayesian approach to digital matting,” in CVPR, 2001, pp. 264–271. [24] A. Levin, D. Lischinski, and Y. Weiss, “A closed-form solution to natural image matting,” IEEE Trans. PAMI, vol. 30, no. 2, pp. 228–242, Feb. 2008. [25] J.Wang and M. F. Cohen, “Optimized color sampling for robust matting,” in CVPR, 2007, pp. 1–8. [26] J. Wang, M. Agrawala, and M. F. Cohen, “Soft scissors: an interactive tool for realtime high quality matting,” ACM Trans. Graph, vol. 26, no. 3, pp. 9, 2007. [27] Y.-Y. Chuang, A. Agarwala, B. Curless, David H. Salesin, and Richard Szeliski, “Video matting of complex scenes,” ACM Trans. Graph, vol. 21, no. 3, pp. 243– 248, July 2002. [28] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” IJCV, vol. 1, no. 4, pp. 321–331, 1988. [29] A. Yilmaz, X. Li, and M. Shah, “Contour-based object tracking with occlusion handling in video acquired using mobile cameras,” IEEE Trans. PAMI, vol. 26, no. 11, pp. 1531–1536, 2004. [30] A. Agarwala, A. Hertzmann, D. H. Salesin, and S. M. Seitz, “Keyframe-based tracking for rotoscoping and animation,” ACM Trans. Graph, vol. 23, no. 3, pp. 584–591, 2004. [31] R. S. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother, “A comparative study of energy minimization methods for markov random fields,” in ECCV, 2006, pp. II: 16–29. [32] C.-H. Teh and R.-T. Chin, “On the detection of dominant points on digital curves,” IEEE Trans. PAMI, vol. 11, pp. 859–872, 1989. [33] V. Kolmogorov and R. Zabih., “What energy functions can be minimized via graph cuts,” IEEE Trans. PAMI, vol. 26, no. 3, pp. 147–159, 2004. [34] R. Zabih, O. Veksler, and Y. Y. Boykov, “Fast approximate energy minimization via graph cuts,” in ICCV, 1999, pp. 377–384. [35] S. Schaefer, T. McPhail, and J. Warren, “Image deformation using moving least squares,” ACM Trans. Graph, vol. 25, no. 3, pp. 533–540, 2006. [36] M. Alexa, D. Cohen-Or, and D. Levin, “As-rigid-as-possible shape interpolation,” in SIGGRAPH, 2000, pp. 157–164. [37] T. Igarashi, T. Moscovich, and J. F. Hughes, “As-rigid-as-possible shape manipulation,” ACM Trans. Graph, vol. 24, no. 3, pp. 1134–1141, 2005. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/40366 | - |
| dc.description.abstract | 隨著數位攝影機的普及，多媒體編輯和分析逐漸引起專家以及一般消費者的興趣。而對於多媒體編輯和分析，景物切割是一項相當重要且基礎的工作。使用者提供給景物切割系統關於前景及背景的提示，然後系統將景物從影片裡分離出來。前人所提出的方法常需要電腦長時間運算或是相當大量的使用者互動，而有些方法為了降低問題的複雜度而假設影片是由景物及靜態背景所組成，但是這些方法的應用卻因此而受到了相當限制。因此，我們提出一個新景物切割系統，整合以馬可夫隨機域為基礎的輪廓追縱法以及圖切景物切割法以減少運算量以及人為提示的需求。本系統利用輪廓追縱傳遞物件的形狀，再利用圖切演算法達到精確的切割。實驗結果顯示我們的演算法能用更少的關鍵影格且更有效率的切割影片裡的物件，因此降低了對使用者的互動的需求。 | zh_TW |
| dc.description.abstract | Video object segmentation is a critical task in multimedia editing and analysis. Normally, the user provides some hints of foreground and background, then the target object is extracted from the video sequence. Most previous methods are computation-expensive or labor-intensive, and some approaches that assume static background have limited applications. In this thesis, we proposed a novel video segmentation system that integrates MRF-based contour tracking with graph-cut image segmentation to reduce the computational cost and the user interaction. The contour tracking propagates the shape of the target object, and the graph-cut refines the shape to improve the accuracy of video segmentation. Experimental results show that our algorithm can efficiently segment the video sequence with less key-frames and hence less user interaction. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-14T16:45:49Z (GMT). No. of bitstreams: 1 ntu-97-R95942043-1.pdf: 24923983 bytes, checksum: b7c2ea171795acfcbbd6d2e73da09350 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | Abstract vii
1 Introduction 1 1.1 Image and Video Object Segmentation . . . . . . . . . . . . . . . . . . . 1 1.2 User Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Challenges of Video Segmentation . . . . . . . . . . . . . . . . . . . . . 3 1.4 Research Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.5 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Background and related work 7 2.1 Video Object Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Automatic Systems . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.2 Interactive Systems . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.3 Matting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2 Contour Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3 Markov Random Field . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3 Video Object Segementation via MRF-Based Contour Tracking 21 3.1 Overview of The Proposed System . . . . . . . . . . . . . . . . . . . . . 21 3.2 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3 MRF-Based Contour Tracking . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.1 Motion Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.2 Energy Minimization . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4 Graph Cut Object Segmentation . . . . . . . . . . . . . . . . . . . . . . 28 3.4.1 Graph Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4.2 Energy Definition . . . . . . . . . . . . . . . . . . . . . . . . . . 30 i ii 3.4.3 Temporal Consistency . . . . . . . . . . . . . . . . . . . . . . . 33 3.4.4 Algorithm Summary and Implementation . . . . . . . . . . . . . 35 4 Experiments 39 5 Discussion and Conclusion 47 | |
| dc.language.iso | en | |
| dc.subject | 馬可夫隨機域 | zh_TW |
| dc.subject | 影像切割 | zh_TW |
| dc.subject | 輪廓追縱 | zh_TW |
| dc.subject | video segmentation | en |
| dc.subject | contour tracking | en |
| dc.subject | MRF | en |
| dc.title | 利用馬可夫隨機域進行輪廓追蹤之景物切割技術 | zh_TW |
| dc.title | Video Object Segmentation via MRF-based Contour Tracking | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 歐陽明(Ming Ouhyoung),洪一平(Yi-Ping Hung),莊永裕(Yung-Yu Chuang) | |
| dc.subject.keyword | 影像切割,輪廓追縱,馬可夫隨機域, | zh_TW |
| dc.subject.keyword | video segmentation,contour tracking,MRF, | en |
| dc.relation.page | 51 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-07-31 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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